In the last 15 years, the development of biohybrid machines (BHM) expanded greatly. They combine living cell actuators with artificial materials in order to achieve greater autonomy, flexibility, and energy efficiency compared to standard robots. However, the available literature shows that BHMs are developed in silos of individual research groups, making their development more of an art relying on personal knowledge, intuition, and skills than on standardized decision-making processes. To scale-up discrete manufacturing of BHM, many challenges should be standardized such as: choosing appropriate cells and materials for building devices, determining operating limitations (e.g. temperature, surrounding medium), expected life span of BHMs, verification & validation (V&V) procedures, control of selective and coordinated actuation, information processing within a device, etc.
To push the manufacturing of BHMs towards bio-intelligent paradigm and model-based engineering, we propose to develop a self-monitoring and self-controlling manufacturing pipeline of BHMs. To realize such a pipeline, we would need to (i) Develop a modeling and simulation framework that will streamline the processes of design and preliminary testing thus speeding-up and reducing cost of steps that would otherwise be done manually. Also, given that actuators in BHMs are living cells, which greatly expands the parameter space, we believe that the development of BHMs would greatly benefit from an AI-guided modeling process to optimize search for the most efficient design; (ii) To experimentally test, optimize, and verify the platform by developing a proof-of-principle reconfigurable modular catheter BHM; (iii) To group all necessary manufacturing equipment into an integrated bio-intelligent manufacturing cell (BIMC) and demonstrate its adaptable operation.
As a proof-of-principle, we will use the BHM catheter as an innovative medical device that would be able to arrive into hard-to-reach regions of the human body and release drugs there. The operating environment in which the catheter will be tested will have embedded sensors to provide real-time information over multiple actuation modes (such as bending and the force exerted on vessel walls) so that the behavior of synthesized BHMs will be monitored and compared with simulation predictions and used for the update of the modeling framework.
